Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning.

Breath odor sensing-based individual authentication was conducted for the first time using an artificial olfactory sensor system. Using a 16-channel chemiresistive sensor array and machine learning, a mean accuracy of >97% was successfully achieved. The impact of the number of sensors on the accuracy and reproducibility was also demonstrated.

Correction: Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning.

Correction for 'Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning' by Chaiyanut Jirayupat et al., Chem. Commun., 2022, DOI: https://doi.org/10.1039/D1CC06384G.

Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry.

We present a method named NPFimg, which automatically identifies multivariate chemo-/biomarker features of analytes in chromatography-mass spectrometry (MS) data by combining image processing and machine learning. NPFimg processes a two-dimensional MS map (m/z vs retention time) to discriminate analytes and identify and visualize the marker features. Our approach allows us to comprehensively characterize the signals in MS data without the conventional peak picking process, which suffers from false peak detections. The feasibility of marker identification is successfully demonstrated in case studies of aroma odor and human breath on gas chromatography-mass spectrometry (GC-MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg, in that it has lower error rates of signal acquisition and marker identification. In addition, we show the potential applicability of NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentially applicable to data processing in diverse metabolomics/chemometrics using GC-MS and liquid chromatography-MS. NPFimg is available as open source on GitHub (http://github.com/poomcj/NPFimg) under the MIT license.

Identification of Genetic Variants via Bacterial Respiration Gas Analysis.

Indole is a signal molecule derived from the conversion of tryptophan, and it is present in bacterial respiratory gas. Besides influencing bacterial growth, indole exhibits effects on human health, including a positive effect on inflammation and protection against pathogens. However, a high fecal indole concentration (FIC) can suggest an unbalanced gut flora or the presence of certain pathogens. To analyze the indole produced by bacteria, its collection and detection is required. Traditional methods usually require centrifugation of liquid bacterial culture medium and subsequent extraction of indole from the medium or partial purification of indole from fecal samples (e.g., by distillation or extraction). In this study, we demonstrate the possibility of identifying gas contents directly from bacteria, and we distinguish the difference in species and their genetics without the need to centrifuge or extract. Using an absorbent sheet placed above a liquid culture, we were able to collect gas content directly from bacteria. Gas chromatography-mass spectrometry (GC-MS) was used for the analysis. The GC-MS results showed a clear peak attributed to indole for wild-type Escherichia coli cells (MG1655 and MC4100 strains), whereas the indole peak was absent in the chromatograms of cells where proteins, part of the indole production pathway from tryptophan (TnaA and TnaB), were not expressed (by using tnaAB-deleted cells). The indole observed was measured to be present in a low nmol-range. This method can distinguish whether the bacterial genome contains the tnaAB gene or not and can be used to collect gas compounds from bacterial cultures quickly and easily. This method is useful for other goals and future research, such as for measurements in restrooms, for food-handling facilities, and for various applications in medical settings.

Piezoelectric-Induced Triboelectric Hybrid Nanogenerators Based on the ZnO Nanowire Layer Decorated on the Au/polydimethylsiloxane-Al Structure for Enhanced Triboelectric Performance.

Here, we demonstrate a novel device structure design to enhance the electrical conversion output of a triboelectric device through the piezoelectric effect called as the piezo-induced triboelectric (PIT) device. By utilizing the piezopotential of ZnO nanowires embedded into the polydimethylsiloxane (PDMS) layer attached on the top electrode of the conventional triboelectric device (Au/PDMS-Al), the PIT device exhibits an output power density of 50 µW/cm2, which is larger than that of the conventional triboelectric device by up to 100 folds under the external applied force of 8.5 N. We found that the effect of the external piezopotential on the top Au electrode of the triboelectric device not only enhances the electron transfer from the Al electrode to PDMS but also boosts the internal built-in potential of the triboelectric device through an external electric field of the piezoelectric layer. Furthermore, 100 light-emitting diodes (LEDs) could be lighted up via the PIT device, whereas the conventional device could illuminate less than 20 LED bulbs. Thus, our results highlight that the enhancement of the triboelectric output can be achieved by using a PIT device structure, which enables us to develop hybrid nanogenerators for various self-power electronics such as wearable and mobile devices.